No Data is No Excuse

by Stacey Barr |

Often, new performance measures can’t be constructed on existing data. We’ve not measured them before, so not thought to collect that kind of data before. Sadly, too many important and useful measures are not brought to life because of too much procrastination in gathering the data. Guest author, Jerry, shares a great example of how to handle this problem, from his experience working with the local government sector.


Once the heavy lifting construction of strategy work is behind you for a bit, it’s time to begin populating your system with measure data.

In my experience, some people get right on it and begin to assemble the data they believe will help them with strategic decision-making. Those people understand that performance measurement is not an ‘exact science’ but rather a process of learning and adjusting over time as the data suggests.

On the other end of the spectrum are people who feel that until some robust, expansive system is developed or purchased to house all this data in order to track and analyze it to the fullest extent, nothing can be tracked.

This problem can and does persist for long periods of time in many organizations after the ‘heavy lift’ construction phase subsides. It’s usually a case of over engineering the data collection process or just an excuse to stall, delay or not collect data at all.  The second possibility suggests something more serious with ‘buy in’ but will surface eventually and must be addressed. I’ll only respond to the over engineering issue here since there are all kinds of things that need to be considered if someone is becoming an obstacle to this kind of progress in your organization.

When encountering the response that a system must be in place to collect even the most basic measure data, its apparent the person delivering it has misunderstood the true intent behind performance measurement and is treating it as an exact science. Sure, we want measure data that tells us something of significance but we aren’t drawing blood and analyzing it on the other side of this discussion either.

This issue occurred in an organization I was working with on performance measurement. A person in a technical role was designated to collect data on service calls from internal employees. The organization was small by most standards but the technical person was convinced that no data could be collected until the tracking system was purchased, installed and thoroughly tested. Initially, I accepted this notion thinking it wouldn’t be long before a system was in place.

One year later, we still had no data on the measure because the system had yet to be determined and installed.  The manager of the technical person came to me to ask my opinion. My advice to the manager was to convince their employee that the performance data could be captured simply by making daily notes on a piece of paper and aggregating it at the end of each month.  The most sophisticated method they would need would be an Excel spreadsheet, which is actually what they decided to use.

The essence of this article is that we don’t have to wait until we have the most sophisticated data management or warehousing capabilities to begin measuring our strategy.  If we’re really looking to improve our business, we’ll get to measurement very quickly and refine it as we learn more.


What’s your opinion about this? Do you agree or disagree? Why? Let’s chat on the Measure Up blog…

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  1. Juan says:

    This is a great post on a difficult topic. A lot of people miss the point that the main reason for gathering data and presenting metrics is to help businesses manage performance and understand what is driving their results.
    I see the two extremes of thIs dilemma:
    Run-on-excel businesSes: all agility and no governance
    Dw dogma: all governance and no agility.
    While the first APPROACH is usually more effective and cheaper than the second apprOach, thEse tactical efforts often become business as usual. Chaos usually resUlts from using the excel approach excessiVely.
    It is possible to get a much better outcome with a well understood process for measuring performace like pump and appropRiate tool selection Based on the use case complexity.

    • Stacey Barr says:

      I agree with you, Juan. If we choose to be agile and get a quick-and-dirty data collection system up and running with Excel, it needs to be considered as a pilot test, and just the first stage of getting the right data capture system designed and set up. But that’s the discipline of follow-through and implementation to completion. I see this isn’t a strong skill set for most humans!! 😉

  2. Hamad says:

    It is true. We are facing the same problem at the Bank of Tanzania. we have the measure “compliance to service level agreement (sla)” which is meat to measure organisation performance but the bank does not have SLA developed on any of it department neither for internal customers nor for externals. for two years now we are waiting for the departments to develop SLA. I do not know for how long we going to wait for that. the organisation performance result “our services are always on time” has never been measured since.

    • Stacey Barr says:

      That’s a good example of this problem. Hamad. I’d suggest you pick just one department who would find it useful to set up a draft SLA to help them make standards clear to all their staff and customers. And treat it as a pilot test; an experiment. Then the data you collect against the SLA will help them improve the SLA too. Just a thought.

  3. Filip Sergeys says:

    see that happing almost every time. i don´t think it´s because people want to hide or protect things. I see two reasons coming back:
    1) the deeply entranched believe that capturing data should be an automatic process in this digital age. because manual capturing = losing time, and “i have more important things to do”
    2) start simple and learn while doing (including making mistakes) is a fearfull principle. People are scared to make mistakes. their entire career path and bonuses have been build on being successful, not making mistakes.

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